ABSTRACT
In this paper we present an approach to generate lists of opinion bearing phrases with their opinion values in a continuous range between -- 1 and 1. Opinion phrases that are considered include single adjectives as well as adjective-based phrases with an arbitrary number of words. The opinion values are derived from user review titles and star ratings, as both can be regarded as summaries of the user's opinion about the product under review. Phrases are organized in trees with the opinion bearing adjective as tree root. For trees with missing branches, opinion values then can be calculated using trees with similar branches but different roots. An example list is produced and compared to existing opinion lists.
- H. Amiri and T.-S. Chua. Mining Slang and Urban Opinion Words and Phrases from cQA Services: An Optimization Approach. In Proceedings of the 5th ACM international conference on Web search and data mining, pages 193--202, 2012. Google ScholarDigital Library
- S. Baccianella, A. Esuli, and F. Sebastiani. SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. In Proceedings of the 7th International Conference on Language Resources and Evaluation, pages 2200--2204, 2010.Google Scholar
- J. Brooke, M. Tofiloski, and M. Taboada. Cross-Linguistic Sentiment Analysis: From English to Spanish. In Proceedings of the International Conference on Recent Advances in Natural Language Processing, pages 50--54, 2009.Google Scholar
- P. Carvalho, L. Sarmento, M. J. Silva, and E. d. Oliveira. Clues for Detecting Irony in User-Generated Contents: Oh...!! It's "so easy";-). In Proceedings of the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, pages 53--56, 2009. Google ScholarDigital Library
- Y. Choi and C. Cardie. Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 793--801, 2008. Google ScholarDigital Library
- S. Clematide and M. Klenner. Evaluation and Extension of a Polarity Lexicon for German. In Proceedings of the 1st Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, pages 7--13, 2010.Google Scholar
- A. Esuli and F. Sebastiani. Determining Term Subjectivity and Term Orientation for Opinion Mining. In Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics, pages 193--200, 2006.Google Scholar
- A. Esuli and F. Sebastiani. SentiWordNet: A Publicly Available Lexical Resource for Opinion Mining. In Proceedings of the 5th International Conference on Language Resources and Evaluation, pages 417--422, 2006.Google Scholar
- N. Hu, P. A. Pavlou, and J. Zhang. Why do Online Product Reviews have a J-shaped Distribution? Overcoming Biases in Online Word-of-Mouth Communication. Marketing Science, 198:7, 2007.Google Scholar
- N. Hu, J. Zhang, and P. A. Pavlou. Overcoming the J-shaped Distribution of Product Reviews. Communications of the ACM, 52:144--147, 2009. Google ScholarDigital Library
- N. Jindal and B. Liu. Review Spam Detection. In Proceedings of the 16th International World Wide Web Conference, pages 1189--1190, 2007. Google ScholarDigital Library
- N. Jindal and B. Liu. Opinion Spam and Analysis. In Proceedings of the 1st ACM International Conference on Web Search and Data Mining, pages 219--230, 2008. Google ScholarDigital Library
- N. Jindal, B. Liu, and E.-P. Lim. Finding Unusual Review Patterns Using Unexpected Rules. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pages 1549--1552, 2010. Google ScholarDigital Library
- M. Klenner, S. Petrakis, and A. Fahrni. Robust Compositional Polarity Classification. In Proceedings of the International Conference on Recent Advances in Natural Language Processing, pages 180--184, 2009.Google Scholar
- E.-P. Lim, V.-A. Nguyen, N. Jindal, B. Liu, and H. W. Lauw. Detecting Product Review Spammers using Rating Behaviors. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pages 939--948, 2010. Google ScholarDigital Library
- B. Liu, M. Hu, and J. Cheng. Opinion Observer: Analyzing and Comparing Opinions on the Web. In Proceedings of the 14th International World Wide Web Conference, pages 342--351, 2005. Google ScholarDigital Library
- B. Liu and L. Zhang. A Survey of Opinion Mining and Sentiment Analysis. In C. C. Aggarwal and C. Zhai, editors, Mining Text Data, pages 415--463. Springer US, 2012.Google ScholarCross Ref
- J. Liu and S. Seneff. Review Sentiment Scoring via a Parse-and-Paraphrase Paradigm. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 161--169, 2009. Google ScholarDigital Library
- M. Marcus, G. Kim, A. Marcinkiewicz, R. MacIntyre, A. Bies, M. Ferguson, K. Katz, and B. Schasberger. The Penn Treebank: Annotating Predicate Argument Structure. In Proceedings of the ARPA Human Language Technology Workshop, pages 114--119, 1994. Google ScholarDigital Library
- G. A. Miller. WordNet: A Lexical Database for English. Communications of the ACM, 38:39--41, 1995. Google ScholarDigital Library
- K. Moilanen and S. Pulman. Sentiment Composition. In Proceedings of the International Conference on Recent Advances in Natural Language Processing, 2007.Google Scholar
- S. M. Mudambi and D. Schuff. What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com. MIS Quarterly, 34:185--200, 2010. Google ScholarCross Ref
- A. Mukherjee, B. Liu, and N. Glance. Spotting Fake Reviewer Groups in Consumer Reviews. In Proceedings of the 21st International World Wide Web Conference, pages 191--200, 2012. Google ScholarDigital Library
- J.-C. Na, T. T. Thet, C. S. G. Khoo, and W. Y. M. Kyaing. Visual Sentiment Summarization of Movie Reviews. In Proceedings of the 13th International Conference on Asia-pacific Digital Libraries, pages 277--287, 2011. Google ScholarDigital Library
- D. Oelke, M. Hao, C. Rohrdantz, D. A. Keim, U. Dayal, L.-E. Haug, and H. Janetzko. Visual Opinion Analysis of Customer Feedback Data. In Proceedings of the IEEE Symposium on Visual Analytics Science and Technology, pages 187--194, 2009.Google ScholarCross Ref
- B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up?: Sentiment Classification using Machine Learning Techniques. In Proceedings of the Conference on Empitical Methods in Natural Language Processing, pages 79--86, 2002. Google ScholarDigital Library
- A.-M. Popescu and O. Etzioni. Extracting Product Features and Opinions from Reviews. In Proceedings of the Human Language Technology Conference, pages 339--346, 2005. Google ScholarDigital Library
- R. Remus, U. Quasthoff, and G. Heyer. SentiWS -- a Publicly Available German-language Resource for Sentiment Analysis. In Proceedings of the 7th International Conference on Language Ressources and Evaluation, pages 1168--1171, 2010.Google Scholar
- E. Riloff and J. Wiebe. Learning Extraction Patterns for Subjective Expressions. In Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pages 105--112, 2003. Google ScholarDigital Library
- H. Takamura, T. Inui, and M. Okumura. Extracting Semantic Orientations of Words using Spin Model. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, pages 133--140, 2005. Google ScholarDigital Library
- H. Tang, S. Tan, and X. Cheng. A survey on sentiment detection of reviews. Expert Systems with Applications, 36:10760--10773, 2009. Google ScholarDigital Library
- P. D. Turney. Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 417--424, 2002. Google ScholarDigital Library
- L. Velikovich, S. Blair-Goldensohn, K. Hannan, and R. McDonald. The viability of web-derived polarity lexicons. In Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 777--785, 2010. Google ScholarDigital Library
- U. Waltinger. GermanPolarityClues: A Lexical Resource for German Sentiment Analysis. In Proceedings of the 7th International Conference on Language Resources and Evaluation, pages 1638--1642, 2010.Google Scholar
- T. Wilson, J. Wiebe, and P. Hoffmann. Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In Proceedings of the Human Language Technology Conference, pages 347--354, 2005. Google ScholarDigital Library
- K. Zhang, Y. Cheng, Y. Xie, D. Honbo, A. Agrawal, D. Palsetia, K. Lee, W.-k. Liao, and A. Choudhary. SES: Sentiment Elicitation System for Social Media Data. In Proceedings of the 11th IEEE International Conference on Data Mining, pages 129--136, 2011. Google ScholarDigital Library
- L. Zhang and B. Liu. Identifying Noun Product Features that Imply Opinions. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, volume 2, pages 575--580, 2011. Google ScholarDigital Library
Index Terms
- A generic approach to generate opinion lists of phrases for opinion mining applications
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